The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and methods for separating signal contributions from two or more chemical species using MRI.
MRI uses the nuclear magnetic resonance (“NMR”) phenomenon to produce images. When a substance such as human tissue is subjected to a uniform magnetic field, such as the so-called main magnetic field, B0, of an MRI system, the individual magnetic moments of the nuclei in the tissue attempt to align with this B0 field, but precess about it in random order at their characteristic Larmor frequency, ω. If the substance, or tissue, is subjected to a so-called excitation electromagnetic field, B1, that is in the plane transverse to the B0 field and that has a frequency near the Larmor frequency, the net aligned magnetic moment, referred to as longitudinal magnetization, may be rotated, or “tipped,” into the transverse plane to produce a net transverse magnetic moment, referred to as transverse magnetization. A signal is emitted by the excited nuclei or “spins,” after the excitation field, B1, is terminated, and this signal may be received and processed to form an image.
Fat quantification using MRI has important clinical applications, including the early diagnosis and quantitative staging of non-alcoholic fatty liver disease (“NAFLD”). Compared to biopsy, which is the current gold standard for quantitative assessment of NAFLD, MRI methods have the advantages of being non-invasive and allowing volumetric coverage of the whole liver. In addition, MRI methods have the potential for reducing sampling variability, cost, and morbidity.
Chemical shift-based fat quantification methods are able to provide measurements of proton density fat fraction, which is a quantitative biomarker for NAFLD and a useful parameter for other clinical purposes. In these methods, several images are acquired with different echo time (“TE”) shifts, typically using a multi-echo spoiled gradient echo (“SPGR”) pulse sequence. Subsequently, separated water and fat images are reconstructed, and fat fraction maps are obtained. In order for the resulting fat fraction maps to accurately measure proton density fat fraction, multiple confounding factors in the acquired echo signals need to be addressed. These confounding factors include B0 inhomogeneities, T1 bias, noise bias, T2 decay, spectral complexity of the fat signal, and phase errors, such as those due to eddy currents.
If not accounted for, phase errors lead to bias in fat fraction estimation in complex-based fat quantification techniques. At low fat fractions, phase errors can introduce a bias of approximately five percent, whereas measurements above 5.56 percent are typically considered abnormal. Therefore, phase errors in the acquired signals may result in clinically relevant errors for the detection and classification of NAFLD.
The presence of confounding factors may also impact the robustness and reproducibility of a biomarker, such as fat fraction. An ideal biomarker should be platform and protocol independent. For example, the estimated values of fat fraction should be independent of changes in the imaging parameters, such as spatial resolution and choice of TEs. That is to say, the estimates fat fraction values should be robust, and should not change if the measurements are made on a different platform.
To overcome the problems of phase errors, magnitude-based methods have been proposed, in which the phase of the acquired signals is discarded and, therefore, all phase errors are removed. Magnitude-based methods have been shown to produce unbiased fat fraction estimates in the presence of phase errors; however, magnitude fitting can result in severe noise amplification, particularly for certain echo time combinations. The reason for this noise amplification is that magnitude fitting discards the phase information from all the acquired echoes.
Currently, the common methods for addressing phase errors assume that the phase errors originate from eddy currents in the MRI system. In these techniques, eddy current corrections are used to compensate for the phase errors. Such methods are time consuming and have variable success. For example, the corrections are often dependent upon the particular MRI system used; thus, comparisons of fat quantification across patients scanned on different MRI systems is problematic and unreliable.
Some attempts have been made at fitting the phase values for the different echo times to a linear regression and to thereby correct those phase values that deviate from the regression by way of a multiplicative correction factor. For example, if a phase error is present at the first echo time, a constant phase value is extrapolated back from phase values at subsequent echo times. While this concept works in principle for phase errors in water signals, the technique is problematic when trying to compensate for phase errors in signal arising from fat, or other chemical species. This technique becomes problematic because signals other than water, such as fat, have more prominent phase variations; therefore, a linear regression will not appropriately extrapolate correct phase values.
It would therefore be desirable to provide a method for chemical species signal separation that addresses phase errors without discarding valuable phase information.